Computer vision may include object recognition, object categorization, object class detection, image classification, etc. Object recognition may describe finding a particular object (e.g., a handbag of a particular make, a face of a particular person, etc.). Object categorization and object class detection may describe finding objects that belong in a particular class (e.g., faces, shoes, cars, etc.). Multimedia data classification may describe assigning an entire multimedia data item to a particular class (e.g., location recognition, texture classification, etc.). Computerized object recognition, detection, and/or classification using multimedia data is challenging because some objects and/or multimedia data items may not belong to a particular class but may be (mis)classified as a label associated with the particular class despite not belonging to the particular class. Accordingly, techniques for accurately determining that an object and/or multimedia data item is not part of a class (i.e., rejecting an object and/or multimedia data item) are useful for improving classification accuracy.
Current techniques for rejecting objects and/or multimedia data items are error prone. One technique for rejecting objects and/or multimedia data items leverages thresholds to reject any object and/or multimedia data item with a post-classification value below a predetermined threshold. However, often times this threshold technique is inaccurate and objects and/or multimedia data items that are associated with a topic are mistakenly rejected. For instance, a user may input a photo of a chi-poo for classification in a “dog” class. Because the chi-poo is difficult to classify, the classifier may output classification values below a predetermined threshold indicating that the chi-poo is not associated with any label in the “dog” class. The chi-poo is a dog, however, and accordingly, such classification is inaccurate.
Other techniques (e.g., n+1 classification techniques) collect positive and negative data. Positive data may include objects and/or multimedia data items that are associated with labels in a class (e.g., dogs). Negative data may include objects and/or multimedia data items that are not associated with any labels in the class (e.g., not a dog). Such techniques train a classifier to recognize objects and/or multimedia data items associated with each of the labels in the class (e.g., recognizing an object and/or multimedia data item is a particular type of dog) and an additional class that is not associated with any label in the class (e.g., recognizing the object and/or multimedia data item is not a dog). However, the negative data may comprise of a very diverse set of labels and classes and accordingly, a single model may not accurately identify objects and/or multimedia data items that do not belong to labels in the class (e.g., dogs).